During the time I have used R the base graphics package has met my needs, although I have been aware of ggplot2 but found learning it a bit of a struggle so have pretty much ignored it until now. Most … Continue reading →
Monthly Archives: March 2012
Missing my Statsy Goodness? Check out #SciFund!
I know, I know, I have been kinda lame about posting here lately. But that’s because my posting muscle has been focused on the new analyses for what makes a succesful #SciFund proposal. I’ve been posting them at the #SciFund blog under the Analysis tag – so check it out. There’s some fun stats, and
Pathway Analysis for High-Throughput Genomics Studies
I get a lot of requests in the core about running a "pathway analysis." Someone ran a handful of gene expression arrays, or better yet, ran an RNA-seq experiment (with replicates!). These, and many other kinds of high-throughput assays (GWAS, ChIP-seq,...
R101
I’m preparing “R101,” an introductory workshop on the statistical software R. Perhaps other beginners might find some use in the following summary and resources. (See also the post on resources for teaching yourself introductory statistics.) Do you have obligatory screenshots … Continue reading →
Screencast: The Making of 17018d5488
The following screencast demonstrates the use of R, the quantmod R package and bash to process SPX data from 1950. An explanation on how to access a git repository that includes the plots and the R console history is also provided. This screencast prod...
Multiple Factor Model – Building 130/30 Index
Nico brought to my attention the 130/30: The New Long-Only (2008) by A. Lo, P. Patel paper in his comment to the Multiple Factor Model – Building CSFB Factors post. This paper presents a very detailed step by step guide to building 130/30 Index using average CSFB Factors as the alpha model and MSCI Barra 
Recovering Marginal Effects and Standard Errors from Interaction Terms in R
When I fit models with interactions, I often want to recover not only the interaction effect but also the marginal effect (the main effect + the interaction) and of course the standard errors. There are a couple of ways to do this in R but I ended writ...
Recovering Marginal Effects and Standard Errors from Interaction Terms in R
When I fit models with interactions, I often want to recover not only the interaction effect but also the marginal effect (the main effect + the interaction) and of course the standard errors. There are a couple of ways to do this in R but I ended writ...
Recovering Marginal Effects and Standard Errors from Interaction Terms in R
When I fit models with interactions, I often want to recover not only the interaction effect but also the marginal effect (the main effect + the interaction) and of course the standard errors. There are a couple of ways to do this in R but I ended writ...
how to build a for loop in r
(This article was first published on twotorials by anthony damico, and kindly contributed to R-bloggers) To leave a comment for the author, please follow the link and comment on his blog: twotorials by anthony damico. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL,...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
